Global parameters for modeling

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R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.3

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggrepel_0.8.0               tidyr_0.8.3                 dplyr_0.8.0.1               cowplot_0.9.4              
 [5] pheatmap_1.0.12             gtools_3.8.1                ggplot2_3.1.1               MASS_7.3-51.4              
 [9] DESeq2_1.22.2               SummarizedExperiment_1.12.0 DelayedArray_0.8.0          BiocParallel_1.16.6        
[13] matrixStats_0.54.0          GenomicRanges_1.34.0        GenomeInfoDb_1.18.2         IRanges_2.16.0             
[17] S4Vectors_0.20.1            vsn_3.50.0                  Biobase_2.42.0              BiocGenerics_0.28.0        
[21] lattice_0.20-38             gplots_3.0.1.1              RColorBrewer_1.1-2          edgeR_3.24.3               
[25] limma_3.38.3               

loaded via a namespace (and not attached):
 [1] bitops_1.0-6           bit64_0.9-7            tools_3.5.2            backports_1.1.4       
 [5] R6_2.4.0               affyio_1.52.0          rpart_4.1-13           KernSmooth_2.23-15    
 [9] Hmisc_4.2-0            DBI_1.0.0              lazyeval_0.2.2         colorspace_1.4-1      
[13] nnet_7.3-12            withr_2.1.2            tidyselect_0.2.5       gridExtra_2.3         
[17] bit_1.1-14             compiler_3.5.2         preprocessCore_1.44.0  htmlTable_1.13.1      
[21] caTools_1.17.1.2       scales_1.0.0           checkmate_1.9.1        genefilter_1.64.0     
[25] affy_1.60.0            stringr_1.4.0          digest_0.6.18          foreign_0.8-71        
[29] rmarkdown_1.12         XVector_0.22.0         base64enc_0.1-3        pkgconfig_2.0.2       
[33] htmltools_0.3.6        htmlwidgets_1.3        rlang_0.3.4            rstudioapi_0.10       
[37] RSQLite_2.1.1          jsonlite_1.6           acepack_1.4.1          RCurl_1.95-4.12       
[41] magrittr_1.5           GenomeInfoDbData_1.2.0 Formula_1.2-3          Matrix_1.2-17         
[45] Rcpp_1.0.1             munsell_0.5.0          stringi_1.4.3          yaml_2.2.0            
[49] zlibbioc_1.28.0        plyr_1.8.4             grid_3.5.2             blob_1.1.1            
[53] gdata_2.18.0           crayon_1.3.4           splines_3.5.2          annotate_1.60.1       
[57] locfit_1.5-9.1         knitr_1.22             pillar_1.3.1           geneplotter_1.60.0    
[61] XML_3.98-1.19          glue_1.3.1             evaluate_0.13          latticeExtra_0.6-28   
[65] data.table_1.12.2      BiocManager_1.30.4     gtable_0.3.0           purrr_0.3.2           
[69] assertthat_0.2.1       xfun_0.6               xtable_1.8-3           rsconnect_0.8.13      
[73] survival_2.44-1.1      tibble_2.1.1           AnnotationDbi_1.44.0   memoise_1.1.0         
[77] cluster_2.0.8          statmod_1.4.30        

Number of raw counts, samples

 [1] "physiologyFW"                            "physiologyM"                            
 [3] "condition15_ppt"                         "cladeClade2"                            
 [5] "cladeClade3"                             "physiologyM:condition15_ppt"            
 [7] "physiologyM:cladeClade2"                 "physiologyM:cladeClade3"                
 [9] "condition15_ppt:cladeClade2"             "condition15_ppt:cladeClade3"            
[11] "physiologyM:condition15_ppt:cladeClade2" "physiologyM:condition15_ppt:cladeClade3"
[1] 12
attr(,"method")
[1] "tolNorm2"
attr(,"useGrad")
[1] FALSE
attr(,"tol")
[1] 1.798561e-14
[1] 81 12
[1] 0.751381

Exploratory Plots

Heatmaps

Individuals clustered by overall expression

Individuals by Top 100 genes heatmap

Individuals clustered by top 100 gene expression

PCA for overall expression

---
title: "DEanalysis_kfish_osmotic"
author: "Lisa Johnson"
date: '`r Sys.Date()`'
output:
  html_document:
    code_folding: hide
    collapsed: no
    df_print: paged
    number_sections: yes
    theme: cerulean
    toc: yes
    toc_depth: 5
    toc_float: yes
  html_notebook:
    toc: yes
    toc_depth: 5
---

```{r GlobalVariables}

#Setting a reasonable p-value threshold to be used throughout

p_cutoff <- 0.05

p_cutoff_new <- 0.05

knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)

FC_cutoff_original <- 0

FC_cutoff_new <- 1

```


#Global parameters for modeling



```{r LoadPackages}

# Install function for packages    
packages<-function(x){
  x<-as.character(match.call()[[2]])
  if (!require(x,character.only=TRUE)){
    install.packages(pkgs=x,repos="http://cran.r-project.org")
    require(x,character.only=TRUE)
  }
}

bioconductors <- function(x){
    x<- as.character(match.call()[[2]])
    if (!require(x, character.only = TRUE)){
      source("https://bioconductor.org/biocLite.R")
      biocLite(pkgs=x)
      require(x, character.only = TRUE)
    }
}

packages(MASS)
packages(ggplot2)
packages(gtools)
packages(pheatmap)
packages(cowplot)
packages(RColorBrewer)
packages(dplyr)
packages(tidyr)
packages(ggrepel)
bioconductors(DESeq2)
bioconductors(limma)
bioconductors('edgeR')
packages(gplots)
packages(lattice)
packages("vsn")

sessionInfo()

```


```{r loadfiles, results='hide', include=FALSE}

# This is the counts with Experimental Design Info in the last 5 rows
setwd("~/Documents/UCDavis/Whitehead/RNAseq_15killifish/DE_scripts/limma")
#if(!file.exists('../../../../Ensembl_species_counts_designfactors.csv')){
#  download.file("https://osf.io/7vp38/download",'Ensembl_species_counts_designfactors.csv')
#}

counts_design <- read.csv("~/Documents/UCDavis/Whitehead/Ensembl_species_counts_designfactors.csv",stringsAsFactors = FALSE)

```

Number of raw counts, samples
```{r designinfo,results='show'}

dim(counts_design)
#[1] 30471   130

# -----------------------
# Format design and counts matrix
# Drop columns with no data
# -----------------------

design <- counts_design[counts_design$Ensembl == 'Empty',]
#design$type <- c("species","native_salinity","clade","group","condition")
drops <- c("X","Ensembl",
           "F_zebrinus_BW_1.quant","F_zebrinus_BW_2.quant",
           "F_zebrinus_FW_1.quant","F_zebrinus_FW_2.quant",
           "F_notti_FW_1.quant","F_notti_FW_2.quant",
           "F_sciadicus_BW_1.quant","F_sciadicus_FW_1.quant","F_sciadicus_FW_2.quant")
transfer_drops <- c("F_sciadicus_transfer_1.quant","F_rathbuni_transfer_1.quant","F_rathbuni_transfer_2.quant","F_rathbuni_transfer_3.quant",
                    "F_grandis_transfer_1.quant","F_grandis_transfer_2.quant","F_grandis_transfer_3.quant",
                    "F_notatus_transfer_1.quant","F_notatus_transfer_2.quant","F_notatus_transfer_3.quant",
                    "F_parvapinis_transfer_1.quant","F_parvapinis_transfer_2.quant",
                    "L_goodei_transfer_1.quant","L_goodei_transfer_2.quant","L_goodei_transfer_3.quant",
                    "F_olivaceous_transfer_1.quant","F_olivaceous_transfer_2.quant",
                    "L_parva_transfer_1.quant","L_parva_transfer_2.quant","L_parva_transfer_3.quant",
                    "F_heteroclitusMDPP_transfer_1.quant","F_heteroclitusMDPP_transfer_2.quant","F_heteroclitusMDPP_transfer_3.quant",
                    "F_similis_transfer_1.quant","F_similis_transfer_2.quant","F_similis_transfer_3.quant",
                    "F_diaphanus_transfer_1.quant","F_diaphanus_transfer_2.quant",
                    "F_chrysotus_transfer_1.quant","F_chrysotus_transfer_2.quant",
                    "A_xenica_transfer_1.quant","A_xenica_transfer_2.quant","A_xenica_transfer_3.quant" ,
                    "F_catanatus_transfer_1.quant","F_catanatus_transfer_2.quant",
                    "F_heteroclitusMDPL_transfer_1.quant","F_heteroclitusMDPL_transfer_2.quant","F_heteroclitusMDPL_transfer_3.quant")
counts<-counts_design[!counts_design$Ensembl == 'Empty',]
rownames(counts)<-counts$Ensembl
design <- design[ , !(names(design) %in% drops)]
counts <- counts[ , !(names(counts) %in% drops)]
design <- design[ , !(names(design) %in% transfer_drops)]
counts <- counts[ , !(names(counts) %in% transfer_drops)]
dim(design)
#[1]  5 81
dim(counts)
gene.names<-rownames(counts)

design[] <- lapply( design, factor)

# --------------------
# design cateogories
# --------------------

species<-as.character(unlist(design[1,]))
physiology<-as.character(unlist(design[2,]))
clade<-as.character(unlist(design[3,]))
condition<-as.character(unlist(design[5,]))
condition_physiology<-as.vector(paste(condition,physiology,sep="."))
condition_physiology_clade <- as.vector(paste(condition_physiology,clade,sep="."))
condition_physiology_clade <- as.vector(paste("group",condition_physiology_clade,sep=""))
cols<-colnames(counts)
ExpDesign <- data.frame(row.names=cols,
                        condition=condition,
                        physiology = physiology,
                        clade = clade,
                        species = species,
                        sample=cols)
ExpDesign
design = model.matrix( ~0 + physiology*condition*clade, ExpDesign)
colnames(design)
# check rank of matrix
Matrix::rankMatrix( design )
dim(design)
```


```{r norm, results="show", fig.width=11, fig.path='figures/', dev=c('png', 'pdf')}
# ---------------

# DE analysis

# ---------------
gene.names<-rownames(counts)
counts<-as.matrix(as.data.frame(sapply(counts, as.numeric)))
rownames(counts)<-gene.names
class(counts)

keep<-filterByExpr(counts,design = design,group=condition_physiology,min.count = 10, min.total.count = 100)
counts.filt <- counts[keep,]
dim(counts.filt)
#write.csv(counts.filt,"../../../21k_counts_filt_30April2019.csv")

genes = DGEList(count = counts.filt, group = condition_physiology_clade)
#genes = calcNormFactors( genes )

vobj = voom( genes, design, plot=TRUE)
lcpm <- cpm(genes$counts, log = TRUE)
boxplot(lcpm, las = 2, main = "")
plot(colSums(t(lcpm)))

#pca = prcomp(t(lcpm))
#names = colnames(lcpm)
#fac= factor(condition_physiology)
#colours = c("red","blue","green","orange")
#xyplot(
#  PC2 ~ PC1, groups=fac, data=as.data.frame(pca$x), pch=16, cex=1.5,
#  panel=function(x, y, ...) {
#    panel.xyplot(x, y, ...);
#    ltext(x=x, y=y, labels=names, pos=1, offset=0.8, cex=1)
#  },
#  aspect = "fill", col=colours
#  #main = draw.key(key = list(rect = list(col = list(col=colours), text = list(levels(fac)), rep = FALSE)))
#)

vwts <- voomWithQualityWeights(genes, design=design, normalization="quantile", plot=TRUE)

corfit <- duplicateCorrelation(vobj,design,block=ExpDesign$species)

corfit$consensus
#[1] 0.751381

fitRan <- lmFit(vobj,design,block=ExpDesign$species,correlation=corfit$consensus)
colnames(coef(fitRan))

# [1] "(Intercept)"                             "physiologyM"                            
# [3] "condition15_ppt"                         "cladeClade2"                            
# [5] "cladeClade3"                             "physiologyM:condition15_ppt"            
# [7] "physiologyM:cladeClade2"                 "physiologyM:cladeClade3"                
# [9] "condition15_ppt:cladeClade2"             "condition15_ppt:cladeClade3"            
# [11] "physiologyM:condition15_ppt:cladeClade2" "physiologyM:condition15_ppt:cladeClade3"

#fitRan <- eBayes(tmp)

#stats<-topTable(fitRan,number=50,sort.by="p")
#topTable(fitRan,number=50,sort.by="p")
```


# Exploratory Plots


## Heatmaps


### Individuals clustered by overall expression


```{r PlainHeatmap, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf')}
counts_round<-round(counts.filt,digits=0)
dds <- DESeqDataSetFromMatrix(countData = counts_round,colData = ExpDesign,design = design)
rld <- vst(dds, blind = FALSE,fitType='local')
sampleDists <- dist(t(assay(rld)))
df <- as.data.frame(colData(dds)[,c("physiology","condition","clade")])
sampleDistMatrix <- as.matrix( sampleDists )
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists,
         col = colors, annotation = df, show_rownames=F)
```

### Individuals by Top 100 genes heatmap



```{r MiniPlainGeneHeatmap, echo=FALSE, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf')}

select100 <- order(rowMeans(counts(dds,normalized=FALSE)),decreasing=TRUE)[1:100]

pheatmap(assay(rld)[select100,], show_rownames=T,clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists, annotation_col=df)


```

### Individuals clustered by top 100 gene expression


```{r MiniPlainHeatmap, echo=FALSE, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf')}

select100 <- order(rowMeans(counts(dds,normalized=FALSE)),decreasing=TRUE)[1:100]

sampleDists <- dist(t(assay(rld)[select100,]))
sampleDistMatrix <- as.matrix( sampleDists )

pheatmap(sampleDistMatrix, show_rownames=T,clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists, annotation_col=df)


```

### PCA for overall expression


```{r plainPCA, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf')}


cowplot::plot_grid( plotPCA(rld, intgroup="condition"),
                    plotPCA(rld, intgroup="physiology"),
                    plotPCA(rld, intgroup="clade"),
                    plotPCA(rld, intgroup=c("clade","physiology","condition"")),
                           align="c", ncol=2)

```

